Full Data Analyst Roadmap:

1. Optimize LinkedIn

Add a friendly, professional profile photo; create a headline that starts with the job title you want (data analyst, not aspiring);

add a banner photo (Canva can help with this); create an about section that goes deeper into your skills and background and why you’re good at what you do (add some personality);

Highlight transferrable skills in your work and education history sections, quantify your impact where applicable; add your portfolio and anything else you want to show off to your featured section (once you have a portfolio);

Use keywords throughout including tools, years of experience, and job titles (data analyst)

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2. Learn Microsoft Excel:

Software: Microsoft Excel Online (it’s not the best, but it will do.)

YouTube & Telegram channels to learn: Teacher’s Tech, Excel for Data Analysis, Kevin Stratvert, Leila Gharani, Alex the Analyst

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3. Learn SQL

Software: MySQL

Free courses: Khan Academy, W3Schools, SQL Bolt, SQL Zoo, Luke Barousse and Alex the Analyst on YouTube

Telegram channel: SQL for data analysis

Practice problems: Hacker Rank, Leet Code, Data Lemur

Games: SQL Island, SQL Murder Mystery

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4. Learn Tableau

Software: Tableau Public

Learn with Tableau Public website and YouTube channels such as Alex the Analyst, Andy Kriebel, and Tableau Tim

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Data Analyst is NOT a Business Analyst.

BOTH Works with DATA,but the way they do it is Vastly Different.

Data Analyst Mainly :-

✔️ Work in DATABASE.
✔️ Use SQL.
✔️ Propose actionable INSIGHTS.
✔️ Report to Team Memeber's.

Business Analyst Mainly :-

✔️ Work in SPREADSHEET.
✔️ Use EXCEL.
✔️ Make Strategic Recommendations.
✔️ Report to Upper Management.

Awareness of the difference's between Data Analyst & Business Analyst.

5. Build a portfolio to showcase your skills

There are a ton of options for hosting portfolios including GitHub Pages, Carrd, and Maven Portfolio Showcase.

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6. Build a great resume

Highlight transferrable skills from past experience, add a project section to showcase your analytics skills, and link your portfolio in the header.

Alex the Analyst did a great video on this like a year ago including a portfolio template I’ve used.

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7. Start applying to jobs

Leverage any connections you have including friends, family, and former colleagues.

If you already work for a company that hires data analysts, transitioning to a data role within the company can be the easiest route.

Once you have the skills, it’s your job to get the attention of recruiters and hiring managers.

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There are a lot of data analyst jobs that don’t use SQL.

There are a lot of jobs that don’t use Tableau/Power BI.

There are even jobs that don’t use Excel/Google sheets.

BUT, the probability you’ll use at least one of those tools in your data analyst job is close to 100%.

That’s why I recommend every data analyst learn SQL, Excel, and Tableau or Power BI.

That way you’re ready for almost all entry-level data analyst jobs.

8 (and forever): keep learning!

The learning doesn’t stop when you start applying for jobs or even when you get one!

Always be learning something new.

Note: even with hard work, this can be a tough, time-intensive process.

Hang in there and keep the faith!

You got this.

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Imp interview Q&A for numpy in Data analyst

1.Creating an array and performing basic operations:
import numpy as np

# Creating a 1D array
array = np.array([1, 2, 3, 4, 5])
print("Original array:", array)

# Adding 10 to each element
array_plus_ten = array + 10
print("Array after adding 10:", array_plus_ten)

# Multiplying each element by 2
array_times_two = array * 2
print("Array after multiplying by 2:", array_times_two)


2. Creating a 2D array and accessing element:
# Creating a 2D array (matrix)
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("Original matrix:\n", matrix)

# Accessing a specific element
element = matrix[1, 2]
print("Element at row 2, column 3:", element)

# Accessing a specific row
row = matrix[1, :]
print("Second row:", row)

# Accessing a specific column
column = matrix[:, 2]
print("Third column:", column)

3.Basic statistical operations on an array:
# Creating an array
data = np.array([10, 20, 30, 40, 50])

# Calculating the mean
mean = np.mean(data)
print("Mean:", mean)

# Calculating the median
median = np.median(data)
print("Median:", median)

# Calculating the standard deviation
std_dev = np.std(data)
print("Standard Deviation:", std_dev)


4.Creating arrays using different functions and reshaping:
# Creating an array of zeros
zeros_array = np.zeros((3, 3))
print("Array of zeros:\n", zeros_array)

# Creating an array of ones
ones_array = np.ones((2, 4))
print("Array of ones:\n", ones_array)

# Creating an array with a range of values
range_array = np.arange(10)
print("Array with a range of values:", range_array)

# Reshaping an array
reshaped_array = range_array.reshape(2, 5)
print("Reshaped array (2x5):\n", reshaped_array)

5. Element-wise operations and broadcasting:
# Creating two arrays
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])

# Element-wise addition
addition = array1 + array2
print("Element-wise addition:", addition)

# Element-wise multiplication
multiplication = array1 * array2
print("Element-wise multiplication:", multiplication)

# Broadcasting: adding a scalar to an array
scalar_addition = array1 + 10
print("Array after adding 10 to each element:", scalar_addition)

Microsoft Excel Shortcut Keys A-Z

https://www.linkedin.com/posts/sql-analysts_microsoft-excel-shortcuts-a-to-z-everyone-activity-7213733019700912129-V1mX?

Data Analyst Remote Job will be posted in this channel today
👇👇
https://t.iss.one/jobs_SQL

Skills need for everyday data analysis jobs

Complete Data Analytics Mastery: From Basics to Advanced 🚀

Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials

Grasp these essentials in just a week to build a solid foundation in data analytics.

Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics

Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.

Ready for the advanced level? Explore cutting-edge concepts:
- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics

These advanced concepts can be mastered in a couple of weeks with focused study and practice.

Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets

Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro.

Best platforms to learn:
- Intro to Data Analysis
- Udacity's Data Analyst Nanodegree
- Intro to Data Visualisation
- SQL courses with Certificate
- Freecodecamp Python Course
- 365DataScience
- Data Analyst Resume Checklist
- Learning SQL FREE Book

Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩‍💻👨‍💻

Join @free4unow_backup for more free resources.

Like this post if it helps 😄❤️

ENJOY LEARNING 👍👍

Some basic concepts regarding data and database

Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no
specific meaning.

Database is an organized collection of the data stored and can be accessed electronically in a computer system.

DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily.

RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements.

SQL is a database query language used for storing and managing data in RDBMS.